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log_outreach

Record outreach activities in your revenue pipeline by tracking communication channels, responses, and follow-up notes for lead management.

Instructions

Log an outreach activity

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
leadIdNo
companyNameYes
channelYes
templateUsedNo
templateIdNo
responseReceivedNo
responseTypeNo
notesNo

Implementation Reference

  • Handler for the log_outreach tool. Dispatches the tool call to the callAPI function with action 'logOutreach' and the provided arguments.
    case "log_outreach":
      result = await callAPI("logOutreach", args);
      break;
  • index.js:216-239 (registration)
    Registration of the log_outreach tool in the tools list, including name, description, and input schema definition.
    {
      name: "log_outreach",
      description: "Log an outreach activity",
      inputSchema: {
        type: "object",
        properties: {
          leadId: { type: "number" },
          companyName: { type: "string" },
          channel: {
            type: "string",
            enum: ["Cold Email", "LinkedIn", "Upwork", "Phone", "Gmail", "Other"]
          },
          templateUsed: { type: "string" },
          templateId: { type: "number" },
          responseReceived: { type: "string", enum: ["Yes", "No"] },
          responseType: {
            type: "string",
            enum: ["Interested", "Not Interested", "Question", "Meeting Booked", "No Response"]
          },
          notes: { type: "string" },
        },
        required: ["companyName", "channel"],
      },
    },
  • Input schema for the log_outreach tool, defining parameters like leadId, companyName, channel, etc.
    inputSchema: {
      type: "object",
      properties: {
        leadId: { type: "number" },
        companyName: { type: "string" },
        channel: {
          type: "string",
          enum: ["Cold Email", "LinkedIn", "Upwork", "Phone", "Gmail", "Other"]
        },
        templateUsed: { type: "string" },
        templateId: { type: "number" },
        responseReceived: { type: "string", enum: ["Yes", "No"] },
        responseType: {
          type: "string",
          enum: ["Interested", "Not Interested", "Question", "Meeting Booked", "No Response"]
        },
        notes: { type: "string" },
      },
      required: ["companyName", "channel"],
    },
  • Helper function callAPI that handles HTTP POST requests to the Google Apps Script API endpoint with the action and arguments. This is where the actual API call for logOutreach is made.
    async function callAPI(action, data = {}) {
      debugLog('=== API CALL START ===');
      debugLog(`Action: ${action}`);
      debugLog(`Data: ${JSON.stringify(data)}`);
    
      try {
        // Build form-encoded body for POST
        const formData = new URLSearchParams();
        formData.append('action', action);
    
        // Add all data fields to form
        for (const [key, value] of Object.entries(data)) {
          if (value !== undefined && value !== null) {
            formData.append(key, value.toString());
          }
        }
    
        const formString = formData.toString();
        debugLog(`FormData: ${formString}`);
        debugLog(`API_URL: ${API_URL}`);
    
        // Use POST with proper content type
        const response = await fetch(API_URL, {
          method: 'POST',
          headers: {
            'Content-Type': 'application/x-www-form-urlencoded',
          },
          body: formString
        });
    
        debugLog(`Response status: ${response.status}`);
        debugLog(`Response ok: ${response.ok}`);
    
        if (!response.ok) {
          debugLog(`Response not OK: ${response.status} ${response.statusText}`);
          throw new Error(`API request failed: ${response.status} ${response.statusText}`);
        }
    
        const text = await response.text();
        debugLog(`Response text length: ${text.length}`);
        debugLog(`Response text: ${text}`);
    
        if (!text) {
          debugLog('ERROR: Empty response from API');
          throw new Error('Empty response from API');
        }
    
        const parsed = JSON.parse(text);
        debugLog(`Parsed successfully: ${JSON.stringify(parsed)}`);
        debugLog('=== API CALL END ===');
        return parsed;
    
      } catch (error) {
        debugLog(`ERROR in callAPI: ${error.message}`);
        debugLog(`ERROR stack: ${error.stack}`);
        throw error;
      }
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. 'Log an outreach activity' implies a write operation but doesn't specify whether this creates new records, updates existing ones, requires permissions, or has side effects. It lacks details on response format, error handling, or system impacts, leaving significant gaps for a tool with 8 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place without redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 8 parameters, 0% schema coverage, no output schema, and no annotations, the description is inadequate. It doesn't explain what constitutes an 'outreach activity', how logging integrates with sibling tools, or what happens after logging. The minimal description fails to provide necessary context for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'leadId', 'channel', 'responseType', or other parameters mean in context, nor does it clarify relationships between parameters like 'templateUsed' and 'templateId'. This leaves all 8 parameters semantically undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Log') and resource ('outreach activity'), making the purpose immediately understandable. It doesn't distinguish from sibling tools like 'log_daily_metrics' or 'add_lead', but it's specific enough to convey the core function without being tautological.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'add_lead', 'update_lead', or 'log_daily_metrics'. There's no mention of prerequisites, timing, or context for logging outreach activities, leaving the agent to infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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